Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID ▲ Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-3211 Conference proceedings 2022 Tsoumas, Ilias; Giannarakis, Georgios; Sitokonstantinou, Vasileios; Koukos, Alkiviadis; Loka, Dimitra; Bartsotas, Nikolaos; Kontoes, Charalampos; Athanasiadis, Ioannis Evaluating Digital Tools for Sustainable Agriculture using Causal Inference NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning 10.48550/arxiv.2211.03195 Control Systems Uncategorized No abstract available 101004152
publications-3212 Other 2022 Md Rashad Al Hasan Rony; Mirza Mohtashim Alam; Semab Ali; Qasid Saleem; Jens Lehmann; Sahar Vahdati LEMON: LanguagE MOdel for Negative Sampling of Knowledge Graph Embeddings 10.21203/rs.3.rs-2188328/v1 Control Systems Uncategorized Abstract Knowledge Graph Embedding models have become an important area of machine learning. Those models provide a latent representation of entities and relations of a knowledge graph which can then be used in downstream machine learning tasks such as link prediction.The learning process of such models can be performed by contrasting positive and negative triples.While the triples of the underlying knowledge graph are considered positive, the generation of the negative samples has its own process. Therefore, the sampling procedures for obtaining the negative triples play a crucial role in the performance and effectiveness of Knowledge Graph Embedding models. Most of the existing techniques draw negative samples from a random distribution of entities of the underlying Knowledge Graph which often includes uninformative negative triples. Different works employ adversarial techniques or generative neural networks for negative sampling, which improve the performance of models with the cost of additional sophisticated mechanisms. In this paper, we propose an approach for generating informative negative samples by utilizing complementary knowledge about entities. Particularly, pre-trained language models are used to form neighborhood clusters by computing the distances between entities to obtain representations of symbolic entities via their complementary textual information. Our proposed approach achieved the biggest leaps in performance over the baseline models KBGAN, NSCaching, Uni-SANS with an absolute difference of +22.38, +6.45 and +7.16, respectively on WN18RR dataset. 101004152
publications-3213 Conference proceedings 2023 Lehmann, JensGattogi, PreetamBhandiwad, DhananjayFerré, SébastienVahdati, Sahar Language Models as Controlled Natural Language Semantic Parsers for Knowledge Graph Question Answering ECAI 2023 10.3233/faia230411 Control Systems Uncategorized We propose the use of controlled natural language as a target for knowledge graph question answering (KGQA) semantic parsing via language models as opposed to using formal query languages directly. Controlled natural languages are close to (human) natural languages, but can be unambiguously translated into a formal language such as SPARQL. Our research hypothesis is that the pre-training of large language models (LLMs) on vast amounts of textual data leads to the ability to parse into controlled natural language for KGQA with limited training data requirements. We devise an LLM-specific approach for semantic parsing to study this hypothesis. To conduct our study, we created a dataset that allows the comparison of one formal and two different controlled natural languages. Our analysis shows that training data requirements are indeed substantially reduced when using controlled natural languages, which is relevant since collecting and maintaining high-quality KGQA semantic parsing training data is very expensive and time-consuming. 101004152
publications-3214 Conference proceedings 2023 Maria Pegia, Björn Þór Jónsson, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris MuseHash: Supervised Bayesian Hashing for Multimodal Image Representation ICMR '23: Proceedings of the 2023 ACM International Conference on Multimedia Retrieval 10.1145/3591106.3592228 Data Management & Analytics Uncategorized No abstract available 101004152
publications-3215 Other 2023 Yasmine BOULFANI and Vincent GAUDISSART COLLABORATIVE PLATFORM FOR PROTOTYPING, DEVELOPING AND EXECUTING EO AND AI BASED SERVICES International Geoscience and Remote Sensing Symposium (IGARSS) 10.5281/zenodo.10454558 Uncategorized Groundwater No abstract available 101004152
publications-3216 Conference proceedings 2023 Nick Pantelidis, Stelios Andreadis, Maria Pegia, Anastasia Moumtzidou, Damianos Galanopoulos, Konstantinos Apostolidis, Despoina Touska, Konstantinos Gkountakos, Ilias Gialampoukidis, Stefanos Vrochidis, Vasileios Mezaris, Ioannis Kompatsiaris VERGE in VBS 2023 MultiMedia Modeling 10.1007/978-3-031-27077-2_55 Data Management & Analytics Groundwater No abstract available 101004152
publications-3217 Conference proceedings 2024 Maria Pegia, Ferran Agullo Lopez, Anastasia Moumtzidou, Alberto Gutierrez-Torre, Björn Þór Jónsson, Josep Lluís Berral García, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris Time-Quality Tradeoff of MuseHash Query Processing Performance. MultiMedia Modeling 10.1007/978-3-031-53311-2_20 Simulation & Modeling Natural Water Bodies No abstract available 101004152
publications-3218 Conference proceedings 2022 Josep Ll. Berral; Oriol Aranda; Juan Luis Dominguez; Jordi Torres Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) 10.1109/ipdpsw55747.2022.00172 Uncategorized River Basins No abstract available 101004152
publications-3219 Conference proceedings 2022 George Choumos; Alkiviadis Koukos; Vasileios Sitokonstantinou; Charalampos Kontoes Towards Space-to-Ground Data Availability for Agriculture Monitoring IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) 10.1109/ivmsp54334.2022.9816335 IoT & Sensors Natural Water Bodies No abstract available 101004152
publications-3220 Conference proceedings 2022 Mojtaba, Nayyeri, Sahar, Vahdati, Tansen, Khan, Mirza, Mohtashim Alam, Lisa, Wenige, Andreas, Behrend, Jens, Lehmann Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion ESWC 2022: The Semantic Web AI & Machine Learning Uncategorized No abstract available 101004152